Central plant control system with device geometric modeling and control

ABSTRACT

A method for operating a subplant included in a central plant includes obtaining instruction-based equipment models associated with devices included in the subplant and comprises operating points that define an operation of the devices, generating, for each instruction-based equipment models, a geometric equipment model using the operating points from a particular instruction-based equipment model, the geometric equipment model defining at least one operating domain associated with the particular device, merging geometric equipment models to form a geometric subplant model, the geometric subplant model defining an operation of the subplant comprising devices associated with the geometric equipment models, receiving a desired operating point comprising a load value, determining, relative to the desired operating point, a nearest operating point on the geometric subplant model, setting the nearest operating point on the geometric subplant model as an actual operating point, and operating the subplant at the actual operating point for the subplant.

BACKGROUND

The present disclosure relates generally to a central plant or centralenergy facility configured to serve the energy loads of a building orcampus. The present disclosure relates more particular to a centralplant with an asset allocator configured to determine a distribution ofenergy loads across various subplants of the central plant based ongeometric models of the devices included in the subplants.

A central plant typically include multiple subplants configured to servedifferent types of energy loads. For example, a central plant mayinclude a chiller subplant configured to serve cooling loads, a heatersubplant configured to serve heating loads, and/or an electricitysubplant configured to serve electric loads. Each subplant may have oneor more devices configured to serve each subplant. A central plantpurchases resources from utilities to run the subplants to meet theloads.

A central plant uses models of the equipment in the central plant inorder to determine operating points for each piece of equipment. It canbe difficult and time-consuming for a user to generate the models for acentral plant.

SUMMARY

One implementation of the present disclosure is a method for operating asubplant included in a central plant. The method includes obtaininginstruction-based equipment model associated with a particular deviceincluded in the subplant and comprises operating points that define anoperation of the particular device, generating, for eachinstruction-based equipment models, a geometric equipment model usingthe operating points from a particular instruction-based equipmentmodel, the geometric equipment model defining at least one operatingdomain associated with the particular device, merging geometricequipment models to form a geometric subplant model, the geometricsubplant model defining an operation of the subplant comprising devicesassociated with the geometric equipment models, receiving a desiredoperating point comprising a load value, determining, relative to thedesired operating point, a nearest operating point on the geometricsubplant model, setting the nearest operating point on the geometricsubplant model as an actual operating point, and operating the subplantat the actual operating point for the subplant.

In some embodiments, the method involves repeating the obtaining,generating, merging, receiving, determining, and setting steps for thesubplants included in the central plant to determine the actualoperating point for each subplant.

In some embodiments, generating the geometric equipment model comprisesusing time-series data points to generate the geometric equipment model.

In some embodiments, the time-series data points further comprisesgrouping data gathered from a real-time data collection process.

In some embodiments, merging the geometric equipment models to form thegeometric subplant model further comprises summing each operating pointincluded in the geometric equipment models.

In some embodiments, the method involves defining destinations of anamount of a particular resource produced by the subplant by appointing aportion of the amount to each destination. A summation of each portionis the amount of the particular resource.

In some embodiments, determining the nearest operating point on thegeometric subplant model further comprises determining a Euclideandistance value between the desired operating point and each of theplurality of operating points included in the geometric subplant model.

In some embodiments, determining the nearest operating point on thegeometric subplant model further comprises determining an operatingpoint with a minimum Euclidean distance value.

Another implementation of the present disclosure is a controller forsubplants comprising devices. The controller includes a geometricmodeling module configured to generate a geometric equipment modeldefining an operation of the devices. The geometric modeling modulecomprises an instruction-based model database configured to storeinstruction-based equipment models, wherein each instruction-basedequipment models is associated with a particular device included thesubplants and comprises operating points that defines an operation ofthe particular device, a geometric model generator configured togenerate the geometric equipment model using the instruction-basedequipment models retrieved from the instruction-based model database, ageometric model merger configured to merge geometric equipment models togenerate a geometric subplant model associated with one subplants, and anearest point analyzer configured to receive a desired operating pointand determine, on the geometric subplant model, a nearest operatingpoint relative to the desired operating point.

In some embodiments, the controller further comprises a destinationspecifier configured to receive the geometric subplant model anddetermine destinations of an amount of a resource produced by the one ormore devices by appointing a portion of the amount to each destination.

In some embodiments, the operating points comprise a plurality of samplepoints gathered by a real-time data collection process.

In some embodiments, the nearest point analyzer is configured todetermine a Euclidean distance value between the desired operating pointand each of the plurality of operating points included in the geometricsubplant model.

In some embodiments, the nearest point analyzer is further configured todetermine the nearest operating point with a minimum Euclidean distance.

Yet another implementation is a method for operating subplants includedin a central plant. The method involves obtaining instruction-basedequipment models associated with a particular device included thesubplants and comprises a plurality of operating points that define anoperation of the particular device, generating a geometric equipmentmodel using the operating points from an instruction-based equipmentmodels, merging two or more geometric equipment models to form ageometric subplant model defining an operation the subplants, receivinga desired operating point comprising a load value, determining, relativeto the desired operating point, a nearest point on the geometricsubplant model, setting the nearest point on the geometric subplantmodel as an actual operating point, and operating the subplants suchthat the subplants produce an amount of a resource defined by the actualoperating point.

In some embodiments, generating the geometric equipment model comprisesusing a plurality of time-series data points to generate the geometricequipment model.

In some embodiments, merging the geometric equipment models to form thegeometric subplant model further comprises summing each of the operatingpoints included in the geometric equipment models.

In some embodiments, the method further comprises defining destinationsof a portion of the amount of a particular resource produced by thesubplants by appointing the portion of the amount to each of thedestinations, wherein a summation of each portion is the amount of theparticular resource.

In some embodiments, determining the nearest point on the geometricsubplant model further comprises determining a Euclidean distance valuebetween the desired operating point and each of the operating pointsincluded in the geometric subplant model.

In some embodiments, determining the nearest point on the geometricsubplant model further comprises determining an operating point with aminimum Euclidean distance value.

In some embodiments, the method further comprises repeating theobtaining, generating, merging, receiving, determining, and settingsteps for each of the one or more subplants included in the centralplant to determine the actual operating point for each of the one ormore subplants.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing of a building equipped with a HVAC system, accordingto an exemplary embodiment.

FIG. 2 is a block diagram of a central plant which can be used to servethe energy loads of the building of FIG. 1, according to an exemplaryembodiment.

FIG. 3 is a block diagram of an airside system which can be implementedin the building of FIG. 1, according to an exemplary embodiment.

FIG. 4 is a block diagram of an asset allocation system includingsources, subplants, storage, sinks, and an asset allocator configured tooptimize the allocation of these assets, according to an exemplaryembodiment.

FIG. 5 is a block diagram of a central plant controller in which theasset allocator of FIG. 4 can be implemented, according to an exemplaryembodiment.

FIG. 6 is a detailed block diagram of a geometric modeling module asimplemented in the central plant controller of FIG. 5, according to anexemplary embodiment.

FIG. 7 is a flowchart illustrating a process of generating a geometricmodel and using the generated geometric model to determine controlactions, according to an exemplary embodiment.

FIG. 8 is a graph illustrating a geometric equipment model, according toan exemplary embodiment.

FIG. 9 is a series of graphs illustrating the merging of two geometricequipment models to form a geometric subplant model, according to anexemplary embodiment.

FIG. 10 is a series of graphs illustrating the merging of two geometricsubplant models to form a geometric central plant model, according to anexemplary embodiment.

FIG. 11 is a series of graphs illustrating the division of a resourceproduced by a subplant between two consumers, according to an exemplaryembodiment.

FIG. 12 is a series of graphs illustrating the location of a nearestoperating relative a desired operating point, according to an exemplaryembodiment.

DETAILED DESCRIPTION Overview

Referring generally to the FIGURES, a central plant with an assetallocator and components thereof are shown, according to variousexemplary embodiments. The asset allocator can be configured to manageenergy assets such as central plant equipment, battery storage, andother types of equipment configured to serve the energy loads of abuilding. The asset allocator can determine an optimal distribution ofheating, cooling, electricity, and energy loads across differentsubplants (i.e., equipment groups) of the central plant capable ofproducing that type of energy.

In some embodiments, the asset allocator is configured to control thedistribution, production, storage, and usage of resources in the centralplant. The asset allocator can be configured to minimize the economiccost (or maximize the economic value) of operating the central plantover a duration of an optimization period. The economic cost may bedefined by a cost function J(x) that expresses economic cost as afunction of the control decisions made by the asset allocator. The costfunction J(x) may account for the cost of resources purchased fromvarious sources, as well as the revenue generated by selling resources(e.g., to an energy grid) or participating in incentive programs.

The asset allocator can be configured to define various sources,subplants, storage, and sinks. These four categories of objects definethe assets of a central plant and their interaction with the outsideworld. Sources may include commodity markets or other suppliers fromwhich resources such as electricity, water, natural gas, and otherresources can be purchased or obtained. Sinks may include the requestedloads of a building or campus as well as other types of resourceconsumers. Subplants are the main assets of a central plant. Subplantscan be configured to convert resource types, making it possible tobalance requested loads from a building or campus using resourcespurchased from the sources. Storage can be configured to store energy orother types of resources for later use.

In some embodiments, the asset allocator performs an optimizationprocess to determine an optimal set of control decisions for each timestep within the optimization period. The control decisions may include,for example, an optimal amount of each resource to purchase from thesources, an optimal amount of each resource to produce or convert usingthe subplants, an optimal amount of each resource to store or removefrom storage, an optimal amount of each resource to sell to resourcespurchasers, and/or an optimal amount of each resource to provide toother sinks. In some embodiments, the asset allocator is configured tooptimally dispatch all campus energy assets (i.e., the central plantequipment) in order to meet the requested heating, cooling, andelectrical loads of the campus for each time step within theoptimization period. These and other features of the asset allocator aredescribed in greater detail below.

The central plant controller also includes a geometric modeling moduleconfigured to generate a geometric model characterizing the operation ofeach device included in the central plant, use the generated geometricmodels to locate a nearest operating point for one or more devices, andoutput the located nearest operating for use in operating the one ormore devices at the nearest operating point, according to someembodiments. Accordingly, the asset allocator communicates with thegeometric modeling module to transmit a load allocation. In someembodiments, the geometric modeling module merges two or more geometricequipment models to generate a geometric subplant model defining theoperational characteristics of a subplant. In some embodiments, thegeometric modeling module merges two or more geometric subplant modelsto generate a geometric central plant model defining the operationalcharacteristics of the central plant.

Building and HVAC System

Referring now to FIG. 1, a perspective view of a building 10 is shown.Building 10 can be served by a building management system (BMS). A BMSis, in general, a system of devices configured to control, monitor, andmanage equipment in or around a building or building area. A BMS caninclude, for example, a HVAC system, a security system, a lightingsystem, a fire alerting system, any other system that is capable ofmanaging building functions or devices, or any combination thereof. Anexample of a BMS which can be used to monitor and control building 10 isdescribed in U.S. patent application Ser. No. 14/717,593 filed May 20,2015, the entire disclosure of which is incorporated by referenceherein.

The BMS that serves building 10 may include a HVAC system 100. HVACsystem 100 can include a plurality of HVAC devices (e.g., heaters,chillers, air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 mayprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 may use the heated or chilled fluid toheat or cool an airflow provided to building 10. In some embodiments,waterside system 120 can be replaced with or supplemented by a centralplant or central energy facility (described in greater detail withreference to FIG. 2). An example of an airside system which can be usedin HVAC system 100 is described in greater detail with reference to FIG.3.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 may use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 can be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104can be transported to AHU 106 via piping 108.

AHU 106 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 may deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and mayprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 may receive input from sensorslocated within AHU 106 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Central Plant

Referring now to FIG. 2, a block diagram of a central plant 200 isshown, according to some embodiments. In various embodiments, centralplant 200 can supplement or replace waterside system 120 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, central plant 200 can include a subsetof the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller 102,pumps, valves, etc.) and may operate to supply a heated or chilled fluidto AHU 106. The HVAC devices of central plant 200 can be located withinbuilding 10 (e.g., as components of waterside system 120) or at anoffsite location such as a central energy facility that serves multiplebuildings.

Central plant 200 is shown to include a plurality of subplants 202-208.Subplants 202-208 can be configured to convert energy or resource types(e.g., water, natural gas, electricity, etc.). For example, subplants202-208 are shown to include a heater subplant 202, a heat recoverychiller subplant 204, a chiller subplant 206, and a cooling towersubplant 208. In some embodiments, subplants 202-208 consume resourcespurchased from utilities to serve the energy loads (e.g., hot water,cold water, electricity, etc.) of a building or campus. For example,heater subplant 202 can be configured to heat water in a hot water loop214 that circulates the hot water between heater subplant 202 andbuilding 10. Similarly, chiller subplant 206 can be configured to chillwater in a cold water loop 216 that circulates the cold water betweenchiller subplant 206 building 10.

Heat recovery chiller subplant 204 can be configured to transfer heatfrom cold water loop 216 to hot water loop 214 to provide additionalheating for the hot water and additional cooling for the cold water.Condenser water loop 218 may absorb heat from the cold water in chillersubplant 206 and reject the absorbed heat in cooling tower subplant 208or transfer the absorbed heat to hot water loop 214. In variousembodiments, central plant 200 can include an electricity subplant(e.g., one or more electric generators) configured to generateelectricity or any other type of subplant configured to convert energyor resource types.

Hot water loop 214 and cold water loop 216 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve thermal energyloads of building 10. The water then returns to subplants 202-208 toreceive further heating or cooling.

Although subplants 202-208 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO₂, etc.) can be used inplace of or in addition to water to serve thermal energy loads. In otherembodiments, subplants 202-208 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to central plant 200 arewithin the teachings of the present disclosure.

Each of subplants 202-208 can include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

In some embodiments, one or more of the pumps in central plant 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines incentral plant 200 include an isolation valve associated therewith.Isolation valves can be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in central plant200. In various embodiments, central plant 200 can include more, fewer,or different types of devices and/or subplants based on the particularconfiguration of central plant 200 and the types of loads served bycentral plant 200.

Still referring to FIG. 2, central plant 200 is shown to include hotthermal energy storage (TES) 210 and cold thermal energy storage (TES)212. Hot TES 210 and cold TES 212 can be configured to store hot andcold thermal energy for subsequent use. For example, hot TES 210 caninclude one or more hot water storage tanks 242 configured to store thehot water generated by heater subplant 202 or heat recovery chillersubplant 204. Hot TES 210 may also include one or more pumps or valvesconfigured to control the flow rate of the hot water into or out of hotTES tank 242.

Similarly, cold TES 212 can include one or more cold water storage tanks244 configured to store the cold water generated by chiller subplant 206or heat recovery chiller subplant 204. Cold TES 212 may also include oneor more pumps or valves configured to control the flow rate of the coldwater into or out of cold TES tanks 244. In some embodiments, centralplant 200 includes electrical energy storage (e.g., one or morebatteries) or any other type of device configured to store resources.The stored resources can be purchased from utilities, generated bycentral plant 200, or otherwise obtained from any source.

Airside System

Referring now to FIG. 3, a block diagram of an airside system 300 isshown, according to some embodiments. In various embodiments, airsidesystem 300 may supplement or replace airside system 130 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 can include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and can be located in or aroundbuilding 10. Airside system 300 may operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bycentral plant 200.

Airside system 300 is shown to include an economizer-type air handlingunit (AHU) 302. Economizer-type AHUs vary the amount of outside air andreturn air used by the air handling unit for heating or cooling. Forexample, AHU 302 may receive return air 304 from building zone 306 viareturn air duct 308 and may deliver supply air 310 to building zone 306via supply air duct 312. In some embodiments, AHU 302 is a rooftop unitlocated on the roof of building 10 (e.g., AHU 106 as shown in FIG. 1) orotherwise positioned to receive both return air 304 and outside air 314.AHU 302 can be configured to operate exhaust air damper 316, mixingdamper 318, and outside air damper 320 to control an amount of outsideair 314 and return air 304 that combine to form supply air 310. Anyreturn air 304 that does not pass through mixing damper 318 can beexhausted from AHU 302 through exhaust damper 316 as exhaust air 322.

Each of dampers 316-320 can be operated by an actuator. For example,exhaust air damper 316 can be operated by actuator 324, mixing damper318 can be operated by actuator 326, and outside air damper 320 can beoperated by actuator 328. Actuators 324-328 may communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 mayreceive control signals from AHU controller 330 and may provide feedbacksignals to AHU controller 330. Feedback signals can include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat can be collected, stored, or used by actuators 324-328. AHUcontroller 330 can be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3, AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 can be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 may communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 may receive a chilled fluid from central plant 200(e.g., from cold water loop 216) via piping 342 and may return thechilled fluid to central plant 200 via piping 344. Valve 346 can bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 may receive a heated fluid from central plant 200(e.g., from hot water loop 214) via piping 348 and may return the heatedfluid to central plant 200 via piping 350. Valve 352 can be positionedalong piping 348 or piping 350 to control a flow rate of the heatedfluid through heating coil 336. In some embodiments, heating coil 336includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, by BMScontroller 366, etc.) to modulate an amount of heating applied to supplyair 310.

Each of valves 346 and 352 can be controlled by an actuator. Forexample, valve 346 can be controlled by actuator 354 and valve 352 canbe controlled by actuator 356. Actuators 354-356 may communicate withAHU controller 330 via communications links 358-360. Actuators 354-356may receive control signals from AHU controller 330 and may providefeedback signals to controller 330. In some embodiments, AHU controller330 receives a measurement of the supply air temperature from atemperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 may also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a setpoint temperature for supplyair 310 or to maintain the temperature of supply air 310 within asetpoint temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by coolingcoil 334 or heating coil 336 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU 330 maycontrol the temperature of supply air 310 and/or building zone 306 byactivating or deactivating coils 334-336, adjusting a speed of fan 338,or a combination of both.

Still referring to FIG. 3, airside system 300 is shown to include abuilding management system (BMS) controller 366 and a client device 368.BMS controller 366 can include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 300, central plant 200,HVAC system 100, and/or other controllable systems that serve building10. BMS controller 366 may communicate with multiple downstream buildingsystems or subsystems (e.g., HVAC system 100, a security system, alighting system, central plant 200, etc.) via a communications link 370according to like or disparate protocols (e.g., LON, BACnet, etc.). Invarious embodiments, AHU controller 330 and BMS controller 366 can beseparate (as shown in FIG. 3) or integrated. In an integratedimplementation, AHU controller 330 can be a software module configuredfor execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information from BMScontroller 366 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 366 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 330 may provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 can include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 can be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 can be a stationary terminal or amobile device. For example, client device 368 can be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 may communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Asset Allocation System

Referring now to FIG. 4, a block diagram of an asset allocation system400 is shown, according to an exemplary embodiment. Asset allocationsystem 400 can be configured to manage energy assets such as centralplant equipment, battery storage, and other types of equipmentconfigured to serve the energy loads of a building. Asset allocationsystem 400 can determine an optimal distribution of heating, cooling,electricity, and energy loads across different subplants (i.e.,equipment groups) capable of producing that type of energy. In someembodiments, asset allocation system 400 is implemented as a componentof central plant 200 and interacts with the equipment of central plant200 in an online operational environment (e.g., performing real-timecontrol of the central plant equipment). In other embodiments, assetallocation system 400 can be implemented as a component of a planningtool (described with reference to FIGS. 7-8) and can be configured tosimulate the operation of a central plant over a predetermined timeperiod for planning, budgeting, and/or design considerations.

Asset allocation system 400 is shown to include sources 410, subplants420, storage 430, and sinks 440. These four categories of objects definethe assets of a central plant and their interaction with the outsideworld. Sources 410 may include commodity markets or other suppliers fromwhich resources such as electricity, water, natural gas, and otherresources can be purchased or obtained. Sources 410 may provideresources that can be used by asset allocation system 400 to satisfy thedemand of a building or campus. For example, sources 410 are shown toinclude an electric utility 411, a water utility 412, a natural gasutility 413, a photovoltaic (PV) field (e.g., a collection of solarpanels), an energy market 415, and source M 416, where M is the totalnumber of sources 410. Resources purchased from sources 410 can be usedby subplants 420 to produce generated resources (e.g., hot water, coldwater, electricity, steam, etc.), stored in storage 430 for later use,or provided directly to sinks 440.

Subplants 420 are the main assets of a central plant. Subplants 420 areshown to include a heater subplant 421, a chiller subplant 422, a heatrecovery chiller subplant 423, a steam subplant 424, an electricitysubplant 425, and subplant N, where N is the total number of subplants420. In some embodiments, subplants 420 include some or all of thesubplants of central plant 200, as described with reference to FIG. 2.For example, subplants 420 can include heater subplant 202, heatrecovery chiller subplant 204, chiller subplant 206, and/or coolingtower subplant 208.

Subplants 420 can be configured to convert resource types, making itpossible to balance requested loads from the building or campus usingresources purchased from sources 410. For example, heater subplant 421may be configured to generate hot thermal energy (e.g., hot water) byheating water using electricity or natural gas. Chiller subplant 422 maybe configured to generate cold thermal energy (e.g., cold water) bychilling water using electricity. Heat recovery chiller subplant 423 maybe configured to generate hot thermal energy and cold thermal energy byremoving heat from one water supply and adding the heat to another watersupply. Steam subplant 424 may be configured to generate steam byboiling water using electricity or natural gas. Electricity subplant 425may be configured to generate electricity using mechanical generators(e.g., a steam turbine, a gas-powered generator, etc.) or other types ofelectricity-generating equipment (e.g., photovoltaic equipment,hydroelectric equipment, etc.).

The input resources used by subplants 420 maybe provided by sources 410,retrieved from storage 430, and/or generated by other subplants 420. Forexample, steam subplant 424 may produce steam as an output resource.Electricity subplant 425 may include a steam turbine that uses the steamgenerated by steam subplant 424 as an input resource to generateelectricity. The output resources produced by subplants 420 may bestored in storage 430, provided to sinks 440, and/or used by othersubplants 420. For example, the electricity generated by electricitysubplant 425 may be stored in electrical energy storage 433, used bychiller subplant 422 to generate cold thermal energy, used to satisfythe electric load 445 of a building, or sold to resource purchasers 441.

Storage 430 can be configured to store energy or other types ofresources for later use. Each type of storage within storage 430 may beconfigured to store a different type of resource. For example, storage430 is shown to include hot thermal energy storage 431 (e.g., one ormore hot water storage tanks), cold thermal energy storage 432 (e.g.,one or more cold thermal energy storage tanks), electrical energystorage 433 (e.g., one or more batteries), and resource type P storage434, where P is the total number of storage 430. In some embodiments,storage 430 include some or all of the storage of central plant 200, asdescribed with reference to FIG. 2. In some embodiments, storage 430includes the heat capacity of the building served by the central plant.The resources stored in storage 430 may be purchased directly fromsources or generated by subplants 420.

In some embodiments, storage 430 is used by asset allocation system 400to take advantage of price-based demand response (PBDR) programs. PBDRprograms encourage consumers to reduce consumption when generation,transmission, and distribution costs are high. PBDR programs aretypically implemented (e.g., by sources 410) in the form of energyprices that vary as a function of time. For example, some utilities mayincrease the price per unit of electricity during peak usage hours toencourage customers to reduce electricity consumption during peak times.Some utilities also charge consumers a separate demand charge based onthe maximum rate of electricity consumption at any time during apredetermined demand charge period.

Advantageously, storing energy and other types of resources in storage430 allows for the resources to be purchased at times when the resourcesare relatively less expensive (e.g., during non-peak electricity hours)and stored for use at times when the resources are relatively moreexpensive (e.g., during peak electricity hours). Storing resources instorage 430 also allows the resource demand of the building or campus tobe shifted in time. For example, resources can be purchased from sources410 at times when the demand for heating or cooling is low andimmediately converted into hot or cold thermal energy by subplants 420.The thermal energy can be stored in storage 430 and retrieved at timeswhen the demand for heating or cooling is high. This allows assetallocation system 400 to smooth the resource demand of the building orcampus and reduces the maximum required capacity of subplants 420.Smoothing the demand also asset allocation system 400 to reduce the peakelectricity consumption, which results in a lower demand charge.

In some embodiments, storage 430 is used by asset allocation system 400to take advantage of incentive-based demand response (IBDR) programs.IBDR programs provide incentives to customers who have the capability tostore energy, generate energy, or curtail energy usage upon request.Incentives are typically provided in the form of monetary revenue paidby sources 410 or by an independent service operator (ISO). IBDRprograms supplement traditional utility-owned generation, transmission,and distribution assets with additional options for modifying demandload curves. For example, stored energy can be sold to resourcepurchasers 441 or an energy grid 442 to supplement the energy generatedby sources 410. In some instances, incentives for participating in anIBDR program vary based on how quickly a system can respond to a requestto change power output/consumption. Faster responses may be compensatedat a higher level. Advantageously, electrical energy storage 433 allowssystem 400 to quickly respond to a request for electric power by rapidlydischarging stored electrical energy to energy grid 442.

Sinks 440 may include the requested loads of a building or campus aswell as other types of resource consumers. For example, sinks 440 areshown to include resource purchasers 441, an energy grid 442, a hotwater load 443, a cold water load 444, an electric load 445, and sink Q,where Q is the total number of sinks 440. A building may consume variousresources including, for example, hot thermal energy (e.g., hot water),cold thermal energy (e.g., cold water), and/or electrical energy. Insome embodiments, the resources are consumed by equipment or subsystemswithin the building (e.g., HVAC equipment, lighting, computers and otherelectronics, etc.). The consumption of each sink 440 over theoptimization period can be supplied as an input to asset allocationsystem 400 or predicted by asset allocation system 400. Sinks 440 canreceive resources directly from sources 410, from subplants 420, and/orfrom storage 430.

Still referring to FIG. 4, asset allocation system 400 is shown toinclude an asset allocator 402. Asset allocator 402 may be configured tocontrol the distribution, production, storage, and usage of resources inasset allocation system 400. In some embodiments, asset allocator 402performs an optimization process determine an optimal set of controldecisions for each time step within an optimization period. The controldecisions may include, for example, an optimal amount of each resourceto purchase from sources 410, an optimal amount of each resource toproduce or convert using subplants 420, an optimal amount of eachresource to store or remove from storage 430, an optimal amount of eachresource to sell to resources purchasers 441 or energy grid 440, and/oran optimal amount of each resource to provide to other sinks 440. Insome embodiments, the control decisions include an optimal amount ofeach input resource and output resource for each of subplants 420.

In some embodiments, asset allocator 402 is configured to optimallydispatch all campus energy assets in order to meet the requestedheating, cooling, and electrical loads of the campus for each time stepwithin an optimization horizon or optimization period of duration h.Instead of focusing on only the typical HVAC energy loads, the conceptis extended to the concept of resource. Throughout this disclosure, theterm “resource” is used to describe any type of commodity purchased fromsources 410, used or produced by subplants 420, stored or discharged bystorage 430, or consumed by sinks 440. For example, water may beconsidered a resource that is consumed by chillers, heaters, or coolingtowers during operation. This general concept of a resource can beextended to chemical processing plants where one of the resources is theproduct that is being produced by the chemical processing plat.

Asset allocator 402 can be configured to operate the equipment of assetallocation system 400 to ensure that a resource balance is maintained ateach time step of the optimization period. This resource balance isshown in the following equation:

Σx _(time)=0∀resources,∀time∈horizon

where the sum is taken over all producers and consumers of a givenresource (i.e., all of sources 410, subplants 420, storage 430, andsinks 440) and time is the time index. Each time element represents aperiod of time during which the resource productions, requests,purchases, etc. are assumed constant. Asset allocator 402 may ensurethat this equation is satisfied for all resources regardless of whetherthat resource is required by the building or campus. For example, someof the resources produced by subplants 420 may be intermediate resourcesthat function only as inputs to other subplants 420.

In some embodiments, the resources balanced by asset allocator 402include multiple resources of the same type (e.g., multiple chilledwater resources, multiple electricity resources, etc.). Definingmultiple resources of the same type may allow asset allocator 402 tosatisfy the resource balance given the physical constraints andconnections of the central plant equipment. For example, suppose acentral plant has multiple chillers and multiple cold water storagetanks, with each chiller physically connected to a different cold waterstorage tank (i.e., chiller A is connected to cold water storage tank A,chiller B is connected to cold water storage tank B, etc.). Given thatonly one chiller can supply cold water to each cold water storage tank,a different cold water resource can be defined for the output of eachchiller. This allows asset allocator 402 to ensure that the resourcebalance is satisfied for each cold water resource without attempting toallocate resources in a way that is physically impossible (e.g., storingthe output of chiller A in cold water storage tank B, etc.).

Asset allocator 402 may be configured to minimize the economic cost (ormaximize the economic value) of operating asset allocation system 400over the duration of the optimization period. The economic cost may bedefined by a cost function J(x) that expresses economic cost as afunction of the control decisions made by asset allocator 402. The costfunction J(x) may account for the cost of resources purchased fromsources 410, as well as the revenue generated by selling resources toresource purchasers 441 or energy grid 442 or participating in incentiveprograms. The cost optimization performed by asset allocator 402 can beexpressed as:

$\underset{x}{\arg \min}{J(x)}$

where J(x) is defined as follows:

${J(x)} = {{\sum\limits_{sources}{\sum\limits_{horizon}{{cost}\left( {{purchase}_{{resource},{time}},{time}} \right)}}} - {\sum\limits_{incentives}{\sum\limits_{horizon}{\text{revenue}({ReservationAmount})}}}}$

The first term in the cost function J(x) represents the total cost ofall resources purchased over the optimization horizon. Resources caninclude, for example, water, electricity, natural gas, or other types ofresources purchased from a utility or other source 410. The second termin the cost function J(x) represents the total revenue generated byparticipating in incentive programs (e.g., IBDR programs) over theoptimization horizon. The revenue may be based on the amount of powerreserved for participating in the incentive programs. Accordingly, thetotal cost function represents the total cost of resources purchasedminus any revenue generated from participating in incentive programs.

Each of subplants 420 and storage 430 may include equipment that can becontrolled by asset allocator 402 to optimize the performance of assetallocation system 400. Subplant equipment may include, for example,heating devices, chillers, heat recovery heat exchangers, coolingtowers, energy storage devices, pumps, valves, and/or other devices ofsubplants 420 and storage 430. Individual devices of subplants 420 canbe turned on or off to adjust the resource production of each subplant420. In some embodiments, individual devices of subplants 420 can beoperated at variable capacities (e.g., operating a chiller at 10%capacity or 60% capacity) according to an operating setpoint receivedfrom asset allocator 402. Asset allocator 402 can control the equipmentof subplants 420 and storage 430 to adjust the amount of each resourcepurchased, consumed, and/or produced by system 400.

In some embodiments, asset allocator 402 minimizes the cost functionwhile participating in PBDR programs, IBDR programs, or simultaneouslyin both PBDR and IBDR programs. For the IBDR programs, asset allocator402 may use statistical estimates of past clearing prices, mileageratios, and event probabilities to determine the revenue generationpotential of selling stored energy to resource purchasers 441 or energygrid 442. For the PBDR programs, asset allocator 402 may use predictionsof ambient conditions, facility thermal loads, and thermodynamic modelsof installed equipment to estimate the resource consumption of subplants420. Asset allocator 402 may use predictions of the resource consumptionto monetize the costs of running the equipment.

Asset allocator 402 may automatically determine (e.g., without humanintervention) a combination of PBDR and/or IBDR programs in which toparticipate over the optimization horizon in order to maximize economicvalue. For example, asset allocator 402 may consider the revenuegeneration potential of IBDR programs, the cost reduction potential ofPBDR programs, and the equipment maintenance/replacement costs thatwould result from participating in various combinations of the IBDRprograms and PBDR programs. Asset allocator 402 may weigh the benefitsof participation against the costs of participation to determine anoptimal combination of programs in which to participate. Advantageously,this allows asset allocator 402 to determine an optimal set of controldecisions that maximize the overall value of operating asset allocationsystem 400.

In some embodiments, asset allocator 402 optimizes the cost functionJ(x) subject to the following constraint, which guarantees the balancebetween resources purchased, produced, discharged, consumed, andrequested over the optimization horizon:

${{{\sum\limits_{sources}{purchase}_{{resources},{time}}} + {\sum\limits_{subplants}{{produces}\left( {x_{{internal},{time}},x_{{external},{time}},v_{{uncontrolled},{time}}} \right)}} - {\sum\limits_{subplants}{{consumes}\left( {x_{{internal},{time}},x_{{external},{time}},v_{{uncontrolled},{time}}} \right)}} + {\sum\limits_{storages}{{discharges}_{resource}\left( {x_{{internal},{time}},x_{{external},{time}}} \right)}} - {\sum\limits_{sinks}{requests}_{resource}}} = {0\mspace{14mu} {\forall{resources}}}},{\forall{{time} \in {horizon}}}$

where x_(internal,time) includes internal decision variables (e.g., loadallocated to each component of asset allocation system 400),x_(external,time) includes external decision variables (e.g., condenserwater return temperature or other shared variables across subplants420), and v_(uncontrolled,time) includes uncontrolled variables (e.g.,weather conditions).

The first term in the previous equation represents the total amount ofeach resource (e.g., electricity, water, natural gas, etc.) purchasedfrom each source 410 over the optimization horizon. The second and thirdterms represent the total production and consumption of each resource bysubplants 420 over the optimization horizon. The fourth term representsthe total amount of each resource discharged from storage 430 over theoptimization horizon. Positive values indicate that the resource isdischarged from storage 430, whereas negative values indicate that theresource is charged or stored. The fifth term represents the totalamount of each resource requested by sinks 440 over the optimizationhorizon. Accordingly, this constraint ensures that the total amount ofeach resource purchased, produced, or discharged from storage 430 isequal to the amount of each resource consumed, stored, or provided tosinks 440.

In some embodiments, additional constraints exist on the regions inwhich subplants 420 can operate. Examples of such additional constraintsinclude the acceptable space (i.e., the feasible region) for thedecision variables given the uncontrolled conditions, the maximum amountof a resource that can be purchased from a given source 410, and anynumber of plant-specific constraints that result from the mechanicaldesign of the plant.

Asset allocator 402 may include a variety of features that enable theapplication of asset allocator 402 to nearly any central plant, centralenergy facility, combined heating and cooling facility, or combined heatand power facility. These features include broadly applicabledefinitions for subplants 420, sinks 440, storage 430, and sources 410;multiples of the same type of subplant 420 or sink 440; subplantresource connections that describe which subplants 420 can sendresources to which sinks 440 and at what efficiency; subplant minimumturndown into the asset allocation optimization; treating electricalenergy as any other resource that must be balanced; constraints that canbe commissioned during runtime; different levels of accuracy atdifferent points in the horizon; setpoints (or other decisions) that areshared between multiple subplants included in the decision vector;disjoint subplant operation regions; incentive based electrical energyprograms; and high level airside models. Incorporation of these featuresmay allow asset allocator 402 to support a majority of the centralenergy facilities that will be seen in the future. Additionally, it willbe possible to rapidly adapt to the inclusion of new subplant types.Some of these features are described in greater detail below.

Broadly applicable definitions for subplants 420, sinks 440, storage430, and sources 410 allow each of these components to be described bythe mapping from decision variables to resources consume and resourcesproduced. Resources and other components of system 400 do not need to be“typed,” but rather can be defined generally. The mapping from decisionvariables to resource consumption and production can change based onextrinsic conditions. Asset allocator 420 can solve the optimizationproblem by simply balancing resource use and can be configured to solvein terms of consumed resource 1, consumed resource 2, produced resource1, etc., rather than electricity consumed, water consumed, and chilledwater produced. Such an interface at the high level allows for themappings to be injected into asset allocation system 400 rather thanneeding them hard coded. Of course, “typed” resources and othercomponents of system 400 can still exist in order to generate themapping at run time, based on equipment out of service.

Incorporating multiple subplants 420 or sinks 440 of the same typeallows for modeling the interconnections between subplants 420, sources410, storage 430, and sinks 440. This type of modeling describes whichsubplants 420 can use resource from which sources 410 and whichsubplants 420 can send resources to which sinks 440. This can bevisualized as a resource connection matrix (i.e., a directed graph)between the subplants 420, sources 410, sinks 440, and storage 430.Examples of such directed graphs are described in greater detail withreference to FIGS. 5A-5B. Extending this concept, it is possible toinclude costs for delivering the resource along a connection and also,efficiencies of the transmission (e.g., amount of energy that makes itto the other side of the connection).

In some instances, constraints arise due to mechanical problems after anenergy facility has been built. Accordingly, these constraints are sitespecific and are often not incorporated into the main code for any ofsubplants 420 or the high level problem itself. Commissioned constraintsallow for such constraints to be added without software updates duringthe commissioning phase of the project. Furthermore, if these additionalconstraints are known prior to the plant build, they can be added to thedesign tool run. This would allow the user to determine the cost ofmaking certain design decisions.

Incentive programs often require the reservation of one or more assetsfor a period of time. In traditional systems, these assets are typicallyturned over to alternative control, different than the typical resourceprice based optimization. Advantageously, asset allocator 402 can beconfigured to add revenue to the cost function per amount of resourcereserved. Asset allocator 402 can then make the reserved portion of theresource unavailable for typical price based cost optimization. Forexample, asset allocator 402 can reserve a portion of a battery assetfor frequency response. In this case, the battery can be used to movethe load or shave the peak demand, but can also be reserved toparticipate in the frequency response program.

Central Plant Controller

Referring now to FIG. 5, a block diagram of a central plant controller500 in which asset allocator 402 can be implemented is shown, accordingto an exemplary embodiment. In various embodiments, central plantcontroller 500 can be configured to monitor and control central plant200, asset allocation system 400, and various components thereof (e.g.,sources 410, subplants 420, storage 430, sinks 440, etc.). Central plantcontroller 500 is shown providing nearest operating points to a buildingmanagement system (BMS) 506. The nearest operating points provided toBMS 506 are control decisions for use by the BMS 506 to operate thevarious devices included in the central plant 200

In some embodiments, BMS 506 is the same or similar to the BMS describedwith reference to FIG. 1. BMS 506 may be configured to monitorconditions within a controlled building or building zone. For example,BMS 506 may receive input from various sensors (e.g., temperaturesensors, humidity sensors, airflow sensors, voltage sensors, etc.)distributed throughout the building and may report building conditionsto central plant controller 500. Building conditions may include, forexample, a temperature of the building or a zone of the building, apower consumption (e.g., electric load) of the building, a state of oneor more actuators configured to affect a controlled state within thebuilding, or other types of information relating to the controlledbuilding. BMS 506 may operate subplants 420 and storage 430 to affectthe monitored conditions within the building and to serve the thermalenergy loads of the building.

BMS 506 may receive control signals from central plant controller 500specifying on/off states, charge/discharge rates, and/or setpoints forthe subplant equipment. BMS 506 may control the equipment (e.g., viaactuators, power relays, etc.) in accordance with the control signalsprovided by central plant controller 500. For example, BMS 506 mayoperate the equipment using closed loop control to achieve the setpointsspecified by central plant controller 500. In various embodiments, BMS506 may be combined with central plant controller 500 or may be part ofa separate building management system. According to an exemplaryembodiment, BMS 506 is a METASYS® brand building management system, assold by Johnson Controls, Inc.

Central plant controller 500 may monitor the status of the controlledbuilding using information received from BMS5606. Central plantcontroller 500 may be configured to predict the thermal energy loads(e.g., heating loads, cooling loads, etc.) of the building for pluralityof time steps in an optimization period (e.g., using weather forecastsfrom a weather service 504). Central plant controller 500 may alsopredict the revenue generation potential of incentive based demandresponse (IBDR) programs using an incentive event history (e.g., pastclearing prices, mileage ratios, event probabilities, etc.) fromincentive programs 502. Central plant controller 500 may generatecontrol decisions that optimize the economic value of operating centralplant 200 over the duration of the optimization period subject toconstraints on the optimization process (e.g., energy balanceconstraints, load satisfaction constraints, etc.). The optimizationprocess performed by central plant controller 500 is described ingreater detail below.

In some embodiments, central plant controller 500 is integrated within asingle computer (e.g., one server, one housing, etc.). In various otherexemplary embodiments, central plant controller 500 can be distributedacross multiple servers or computers (e.g., that can exist indistributed locations). In another exemplary embodiment, central plantcontroller 500 may have integrated with a smart building manager thatmanages multiple building systems and/or combined with BMS 506.

Central plant controller 500 is shown to include a communicationsinterface 536 and a processing circuit 507. Communications interface 536may include wired or wireless interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with various systems, devices, ornetworks. For example, communications interface 536 may include anEthernet card and port for sending and receiving data via anEthernet-based communications network and/or a WiFi transceiver forcommunicating via a wireless communications network. Communicationsinterface 536 may be configured to communicate via local area networksor wide area networks (e.g., the Internet, a building WAN, etc.) and mayuse a variety of communications protocols (e.g., BACnet, IP, LON, etc.).

Communications interface 536 may be a network interface configured tofacilitate electronic data communications between central plantcontroller 500 and various external systems or devices (e.g., BMS 506,subplants 420, storage 430, sources 410, etc.). For example, centralplant controller 500 may receive information from BMS 506 indicating oneor more measured states of the controlled building (e.g., temperature,humidity, electric loads, etc.) and one or more states of subplants 420and/or storage 430 (e.g., equipment status, power consumption, equipmentavailability, etc.). Communications interface 536 may receive inputsfrom BMS 506, subplants 420, and/or storage 430 and may provideoperating parameters (e.g., on/off decisions, setpoints, etc.) tosubplants 420 and storage 430 via BMS 506. The operating parameters maycause subplants 420 and storage 430 to activate, deactivate, or adjust asetpoint for various devices thereof.

Still referring to FIG. 5, processing circuit 507 is shown to include aprocessor 508 and memory 510. Processor 508 may be a general purpose orspecific purpose processor, an application specific integrated circuit(ASIC), one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable processing components.Processor 508 may be configured to execute computer code or instructionsstored in memory 510 or received from other computer readable media(e.g., CDROM, network storage, a remote server, etc.).

Memory 510 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 510 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory510 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 510 may be communicably connected toprocessor 508 via processing circuit 507 and may include computer codefor executing (e.g., by processor 508) one or more processes describedherein.

Memory 510 is shown to include a building status monitor 524. Centralplant controller 500 may receive data regarding the overall building orbuilding space to be heated or cooled by system 400 via building statusmonitor 524. In an exemplary embodiment, building status monitor 524 mayinclude a graphical user interface component configured to providegraphical user interfaces to a user for selecting building requirements(e.g., overall temperature parameters, selecting schedules for thebuilding, selecting different temperature levels for different buildingzones, etc.).

Central plant controller 500 may determine on/off configurations andoperating setpoints to satisfy the building requirements received frombuilding status monitor 524. In some embodiments, building statusmonitor 524 receives, collects, stores, and/or transmits cooling loadrequirements, building temperature setpoints, occupancy data, weatherdata, energy data, schedule data, and other building parameters. In someembodiments, building status monitor 524 stores data regarding energycosts, such as pricing information available from sources 410 (energycharge, demand charge, etc.).

Still referring to FIG. 5, memory 510 is shown to include a load/ratepredictor 522. Load/rate predictor 522 may be configured to predict thethermal energy loads (

_(k)) of the building or campus for each time step k (e.g., k=1 . . . n)of an optimization period. Load/rate predictor 522 is shown receivingweather forecasts from a weather service 504. In some embodiments,load/rate predictor 522 predicts the thermal energy loads

_(k) as a function of the weather forecasts. In some embodiments,load/rate predictor 522 uses feedback from BMS 506 to predict loads

_(k). Feedback from BMS 506 may include various types of sensory inputs(e.g., temperature, flow, humidity, enthalpy, etc.) or other datarelating to the controlled building (e.g., inputs from a HVAC system, alighting control system, a security system, a water system, etc.).

In some embodiments, load/rate predictor 522 receives a measuredelectric load and/or previous measured load data from BMS 506 (e.g., viabuilding status monitor 524). Load/rate predictor 522 may predict loads

_(k) as a function of a given weather forecast ({circumflex over(ϕ)}_(w)), a day type (day), the time of day (t), and previous measuredload data (Y_(k-1)). Such a relationship is expressed in the followingequation:

_(k) =f({circumflex over (ϕ)}_(w) ,day,t|Y _(k-1))

In some embodiments, load/rate predictor 522 uses a deterministic plusstochastic model trained from historical load data to predict loads

_(k). Load/rate predictor 522 may use any of a variety of predictionmethods to predict loads

_(k) (e.g., linear regression for the deterministic portion and an ARmodel for the stochastic portion). Load/rate predictor 522 may predictone or more different types of loads for the building or campus. Forexample, load/rate predictor 522 may predict a hot water load

_(Hot,k) and a cold water load

_(Cold,k) for each time step k within the prediction window. In someembodiments, load/rate predictor 522 makes load/rate predictions usingthe techniques described in U.S. patent application Ser. No. 14/717,593.

Load/rate predictor 522 is shown receiving utility rates from sources410. Utility rates may indicate a cost or price per unit of a resource(e.g., electricity, natural gas, water, etc.) provided by sources 410 ateach time step k in the prediction window. In some embodiments, theutility rates are time-variable rates. For example, the price ofelectricity may be higher at certain times of day or days of the week(e.g., during high demand periods) and lower at other times of day ordays of the week (e.g., during low demand periods). The utility ratesmay define various time periods and a cost per unit of a resource duringeach time period. Utility rates may be actual rates received fromsources 410 or predicted utility rates estimated by load/rate predictor522.

In some embodiments, the utility rates include demand charges for one ormore resources provided by sources 410. A demand charge may define aseparate cost imposed by sources 410 based on the maximum usage of aparticular resource (e.g., maximum energy consumption) during a demandcharge period. The utility rates may define various demand chargeperiods and one or more demand charges associated with each demandcharge period. In some instances, demand charge periods may overlappartially or completely with each other and/or with the predictionwindow. Advantageously, demand response optimizer 630 may be configuredto account for demand charges in the high level optimization processperformed by asset allocator 402. Sources 410 may be defined bytime-variable (e.g., hourly) prices, a maximum service level (e.g., amaximum rate of consumption allowed by the physical infrastructure or bycontract) and, in the case of electricity, a demand charge or a chargefor the peak rate of consumption within a certain period. Load/ratepredictor 522 may store the predicted loads,

_(k) and the utility rates in memory 510 and/or provide the predictedloads

_(k) and the utility rates to demand response optimizer 530.

Still referring to FIG. 5, memory 510 is shown to include an incentiveestimator 520. Incentive estimator 520 may be configured to estimate therevenue generation potential of participating in various incentive-baseddemand response (IBDR) programs. In some embodiments, incentiveestimator 520 receives an incentive event history from incentiveprograms 502. The incentive event history may include a history of pastIBDR events from incentive programs 502. An IBDR event may include aninvitation from incentive programs 502 to participate in an IBDR programin exchange for a monetary incentive. The incentive event history mayindicate the times at which the past IBDR events occurred and attributesdescribing the IBDR events (e.g., clearing prices, mileage ratios,participation requirements, etc.). Incentive estimator 520 may use theincentive event history to estimate IBDR event probabilities during theoptimization period.

Incentive estimator 520 is shown providing incentive predictions todemand response optimizer 530. The incentive predictions may include theestimated IBDR probabilities, estimated participation requirements, anestimated amount of revenue from participating in the estimated IBDRevents, and/or any other attributes of the predicted IBDR events. Demandresponse optimizer 530 may use the incentive predictions along with thepredicted loads

_(k) and utility rates from load/rate predictor 522 to determine anoptimal set of control decisions for each time step within theoptimization period.

Still referring to FIG. 5, memory 510 is shown to include a demandresponse optimizer 530. Demand response optimizer 530 may perform anoptimization process to optimize the performance of asset allocationsystem 400. For example, demand response optimizer 530 is shown toinclude asset allocator 402 and a geometric modeling module 518. Assetallocator 402 may control an outer (e.g., subplant level) loop of thecascaded optimization. Asset allocator 402 may determine an optimal loadallocation for each time step in the prediction window in order tooptimize (e.g., maximize) the value of operating asset allocation system400. Load allocation made by asset allocator 402 may include, forexample, load setpoints for each of subplants 420, charge/dischargerates for each of storage 430, and/or resource purchase amounts for eachtype of resource purchased from sources 410. In other words, the loadallocation may define resource allocation at each time step. The loadallocation made by asset allocator 402 are based on the statisticalestimates of incentive event probabilities and revenue generationpotential for various IBDR events as well as the load and ratepredictions. The load allocation made by asset allocator 402 aretransmitted to geometric modeling module 518 for use in determiningnearest operating points of one more devices relative the loadallocation.

Geometric modeling module 518 is configured to generate one or moregeometric models (e.g., of the devices included in a central plant, ofthe subplants included in a central plant, of a central plant) and usethe generated geometric models to determine, for each time step in aprediction window, a nearest operating point for one or more devicesbased on the asset allocation generated for each time step by assetallocator 402. As will be described in greater detail below, geometricmodeling module 518 stores instruction-based equipment models thatdefine the operational characteristics of one or more device included ina central plant and uses each instruction-based model to generate ageometric model of each device. Further, in some embodiments, thegeometric modeling module 518 is configured to use the generatedgeometric equipment models to generate a geometric subplant model foreach subplant included in a central plant. In such embodiments, thegeometric subplant model is used to locate a nearest operating point onthe geometric subplant model relative an asset allocation determined byasset allocator 402. In turn, a nearest operating point is located on atleast one of the geometric equipment models that make up part of thegeometric subplant model. It should be understood that the descriptionof geometric modeling module 518 generating geometric equipment modelsto form a geometric subplant model is intended for exemplary purposesonly. In some embodiments, geometric modeling module 518 is configuredto generate a conglomerate geometric subplant model using two or moregeometric subplant models. As such, geometric modeling module 518 mayfurther be configured to generate geometric models for any device forwhich an instruction-based equipment model is known.

Still referring to FIG. 5, memory 510 is shown to include a subplantcontrol module 528. Subplant control module 658 may store historicaldata regarding past operating statuses, past operating setpoints, andinstructions for calculating and/or implementing control parameters forsubplants 420 and storage 430. Subplant control module 528 may alsoreceive, store, and/or transmit data regarding the conditions ofindividual devices of the subplant equipment, such as operatingefficiency, equipment degradation, a date since last service, a lifespanparameter, a condition grade, or other device-specific data. Subplantcontrol module 528 may receive data from subplants 420, storage 430,and/or BMS 506 via communications interface 536. Subplant control module528 may also receive and store various information from geometricmodeling module 518 such as generated geometric models (e.g., devicegeometric models, subplant geometric models, etc.) and operatingsetpoints.

Data and processing results from demand response optimizer 530, subplantcontrol module 528, or other modules of central plant controller 500 maybe accessed by (or pushed to) monitoring and reporting applications 526.Monitoring and reporting applications 526 may be configured to generatereal time “system health” dashboards that can be viewed and navigated bya user (e.g., a system engineer). For example, monitoring and reportingapplications 526 may include a web-based monitoring application withseveral graphical user interface (GUI) elements (e.g., widgets,dashboard controls, windows, etc.) for displaying key performanceindicators (KPI) or other information to users of a GUI. In addition,the GUI elements may summarize relative energy use and intensity acrossenergy storage systems in different buildings (real or modeled),different campuses, or the like. Other GUI elements or reports may begenerated and shown based on available data that allow users to assessperformance across one or more energy storage systems from one screen.The user interface or report (or underlying data engine) may beconfigured to aggregate and categorize operating conditions by building,building type, equipment type, and the like. The GUI elements mayinclude charts or histograms that allow the user to visually analyze theoperating parameters and power consumption for the devices of the energystorage system.

Still referring to FIG. 5, central plant controller 500 may include oneor more GUI servers, web services 512, or GUI engines 514 to supportmonitoring and reporting applications 526. In various embodiments,applications 526, web services 512, and GUI engine 514 may be providedas separate components outside of central plant controller 500 (e.g., aspart of a smart building manager). Central plant controller 500 may beconfigured to maintain detailed historical databases (e.g., relationaldatabases, XML databases, etc.) of relevant data and includes computercode modules that continuously, frequently, or infrequently query,aggregate, transform, search, or otherwise process the data maintainedin the detailed databases. Central plant controller 500 may beconfigured to provide the results of any such processing to otherdatabases, tables, XML files, or other data structures for furtherquerying, calculation, or access by, for example, external monitoringand reporting applications.

Central plant controller 500 is shown to include configuration tools516. Configuration tools 516 can allow a user to define (e.g., viagraphical user interfaces, via prompt-driven “wizards,” etc.) howcentral plant controller 500 should react to changing conditions in theenergy storage subsystems. In an exemplary embodiment, configurationtools 516 allow a user to build and store condition-response scenariosthat can cross multiple energy storage system devices, multiple buildingsystems, and multiple enterprise control applications (e.g., work ordermanagement system applications, entity resource planning applications,etc.). For example, configuration tools 516 can provide the user withthe ability to combine data (e.g., from subsystems, from eventhistories) using a variety of conditional logic. In varying exemplaryembodiments, the conditional logic can range from simple logicaloperators between conditions (e.g., AND, OR, XOR, etc.) to pseudo-codeconstructs or complex programming language functions (allowing for morecomplex interactions, conditional statements, loops, etc.).Configuration tools 516 can present user interfaces for building suchconditional logic. The user interfaces may allow users to definepolicies and responses graphically. In some embodiments, the userinterfaces may allow a user to select a pre-stored or pre-constructedpolicy and adapt it or enable it for use with their system.

Geometric Modeling Module

Referring now to FIG. 6, a block diagram illustrating geometric modelingmodule 518 in greater detail is shown, according to an exemplaryembodiment. As previously described, geometric modeling module 518 isconfigured to generate one or more geometric models (e.g., of thedevices included in a central plant, of the subplants included in acentral plant, of a central plant) and use the generated geometricmodels to determine, for each time step in a prediction window, anearest operating point for one or more devices relative to a subplantload allocation generated for that time step. In some embodiments,geometric modeling module 518 receives the generated subplant loadallocation from asset allocator 402.

Geometric modeling module 518 is shown to include an instruction-basedmodel database 602 (herein referred to as IBMD 602), according to someembodiments. IBMD 602 operates as a database configured to store, foreach piece of equipment in a central plant, an instruction-basedequipment model that characterizes the operation of a particular pieceof equipment. The various equipment models stored in IBMD 602 can beadded, removed, or otherwise updated based on equipment additions orchanges in a central plant. In some embodiments, one or more of theequipment models stored in IBMD 602 are models provided by amanufacturer of one or more individual pieces of equipment. In someembodiments, the equipment models stored in IBMD 602 are comprised ofoperational data points that are collected in real time to characterizethe operation of a piece of equipment. In some embodiments, theequipment models stored in IBMD 602 are functions that characterize theoperation of a device. For example, a Gordon-Ng performance modelequation may be stored for a chiller device while a number of transferunits (NTU) effectiveness model is stored for a cooling tower. In someembodiments, the equipment models stored in IBMD 602 are modelsgenerated from real-time data collection during operation. In someembodiments, IBMD 602 is configured to store previously-generatedgeometric models. IBMD 602 is configured to output a requestedinstruction-based model to a geometric model generator 604.

Geometric model generator 604 is shown to receive the requestedinstruction-based models from IBMD 602, according to some embodiments.As will be described in greater detail with reference to FIGS. 7 and 8,the geometric model generator 604 is configured to use the requestedinstruction-based equipment model to generate a geometric equipmentmodel for the particular piece of equipment with which the requestedinstruction-based equipment model is associated. In general, geometricmodel generator 604 generates a geometric equipment model by using thereceived instruction-based model associated with a particular device anddetermines, for each time step in an optimization period, a set ofsample data points comprising an independent variable (e.g., loadallocation) and one or more dependent variables. Geometric modelgenerator 604 may be configured to generate, for each individual pieceof equipment in a central plant, a geometric equipment model. In someembodiments, geometric model generator 604 is configured to generate anew geometric model for a particular device based on changes, removals,or additions of one or more instruction-based models stored in IBMD 602.For example, a new chiller having a different equipment model than oneor more other chillers is installed into a central plant. As such,geometric model generator 604 generates a geometric model of the newchiller following installation/storage of the instruction-based modelfor the new chiller in IBMD 602. Geometric model generator 604 is shownto output the generated geometric models to a geometric model merger606.

Geometric model merger 606 is shown to receive the generated geometricequipment models from geometric model generator 604. As will bedescribed in greater detail with reference to FIGS. 7-9, the geometricmodel merger 606 is configured to use the generated geometric equipmentmodels to generate a subplant geometric model by merging two or moregenerated geometric equipment models. In some embodiments, geometricmodel merger 606 is configured to label, rename, or otherwise flag asingle geometric equipment model as a geometric subplant modelcomprising only one piece of equipment for which the single geometricequipment model is generated. For example, a chiller subplant includesone chiller device. As such, upon receiving the geometric equipmentmodel for the one chiller device, geometric model merger 606 renames thegeometric equipment model for the one chiller device as a geometricsubplant model (e.g., geometric model merger 606 does not mergeadditional geometric equipment models with the geometric equipment modelfor the one chiller device). Further, in some embodiments, geometricmodel merger 606 is configured to use the merged geometric equipmentmodels (e.g., a geometric subplant model) to generate a geometriccentral plant model by merging two or more geometric subplant models.Geometric model merger 606 is shown to output the merged geometricmodels to a destination specifier 608.

Destination specifier 608 is shown to receive the merged geometricmodels from geometric model merger 606 and a load allocation for one ormore particular time steps in an optimization window. As previouslydescribed, the load allocation received by destination specifier 608 maybe determined by asset allocator 402 and transmitted to destinationspecifier 608 as a vector solution, wherein the dimensionality of thevector solution is dependent upon on the number of different types ofresources that are produced by a subplant. For example, a chillersubplant operating to produce chilled water will receive aone-dimensional vector solution. In another example, a subplantoperating to produce electricity and steam will receive a two-dimensionvector solution. As will be described in greater detail below,destination specifier 608 is configured to use the merged geometricmodels (e.g., geometric subplant models) and the load allocation todetermine an amount of the load allocation each consumer of a resourcemay consume. For example, if a chiller subplant feeds chilled water totwo chilled water loads, a specified output amount of chilled water tobe feed to each chilled water load may be divided (e.g., equally ornon-equally) between the two chilled water loads by destinationspecifier 608. In some embodiments, a user inputs two or moredestinations to divide an amount of the produced resource between.Destination specifier 608 is shown to output the specified loads foreach destination of a particular resource to a nearest point analyzer610.

Nearest point analyzer 610 is shown to receive the specified loads fromdevice specifier 607, according to some embodiments. Nearest pointanalyzer 610 is configured to determine a nearest operating pointrelative a desired operating point) defined by the received specifiedload. In general, nearest point analyzer 610 determines the nearestoperating by calculating a Euclidean distance between each operatingpoint located on the geometric model and the desired operating point.The features of nearest point analyzer 610 will be described in greaterdetail below with reference to FIGS. 7 and 12. Nearest point analyzer610 is shown to output the determined nearest operating point to BMSsystem 506 for use in controlling the devices included in the subplants.

Method of Generating and Using Geometric Models to Control Subplants

Referring now to FIG. 7, a process 700 for generating geometric modelsand using the geometric models to determine a nearest operating point ona particular geometric model relative a desired operating point isshown, according to some embodiments. In general, process 700 can beperformed by geometric modeling module 518 and modules included thereinto generate one or more geometric equipment models, merge the one ormore geometric equipment models to generate a geometric subplant model,and use the geometric subplant models to determine a nearest operatingpoint for one or more pieces of equipment included in a particularsubplant for which the geometric subplant model was created. As will bedescribed below, various steps of process 700 can be repeated for someor all of the subplants (and devices included therein) included in acentral plant. Further, various steps of process 700 can repeated forone or more time steps in an optimization period to determine controlactions based on the nearest operating point provided by the geometricsubplant model.

Process 700 is shown to include receiving an instruction-based equipmentmodel for a device included in a central plant (step 702), according tosome embodiments. In some embodiments, the instruction-based equipmentmodel is retrieved from IBMD 602 and transmitted to geometric modelgenerator 604. In general, an instruction-based equipment model can beexpressed as:

y=f(x,p,c)

where y is an array of outputs based on three arrays of inputs, xrepresents the independent variable to the instruction-based equipmentmodel, p represents one or more dynamic parameters of theinstruction-based equipment model, and c represents one or more staticconstraints of the instruction-based equipment model provided to theinstruction-based model equipment model at creation of theinstruction-based model. For example, in the case of a Gordon-Ngchiller, the instruction-based equipment model can be expressed as:

${{\frac{T_{ei}}{T_{ci}}\left( {1 + \frac{1}{COP}} \right)} - 1} = {{\frac{T_{ei}}{Q_{e}}\Delta S_{T}} + {Q_{leak}\frac{\left( {T_{ci} - T_{ei}} \right)}{T_{ci}Q_{e}}} + {\frac{RQ_{e}}{T_{ci}}\left( {1 + \frac{1}{COP}} \right)}}$

where the independent variable to the instruction-based equipment modelis Q_(e) (the load allocated to the chiller device), dynamic parametersof the equipment model are T_(ei) and T_(ci) (input temperature of theworking fluid such as water, glycol, etc.) entering the evaporator andcondenser, respectively, and the static constraint is COP (coefficientof performance of the chiller).

In some embodiments, step 702 involves collecting real-time data pointsof an operating device. In such embodiments, and as will be described ingreater detail below, the real-time data points collected are used togenerate one or more operating regions comprising some or all of thereal-time data points that characterize the operation of a particulardevice. An example of real-time data points collected includes a chilledwater load produced by a chiller device and the amount of electricalpower consumed by the chiller to produce such a load of chilled water.

Process 700 is shown to include analyzing the received instruction-basedequipment model by collecting sample points at a configurable time stepto generate the geometric equipment model (step 704), according to someembodiments. In some embodiments, the instruction-based equipment modelis analyzed by geometric model generator 604. Analyzing the receivedinstruction-based equipment model involves collecting time-series databased on the instruction-based equipment model to generate a set ofsample points comprising the independent variable (e.g., chiller coolingload for a chiller device) and the power consumed by a device to producean amount of the independent variable (e.g., electrical power for achiller device), according to some embodiments. The time step for whichdata is configurable based on user preference, model precision/accuracy,equipment run-time, etc. For example, it may desirable to have a shortertime step for a first device whose run-time is less than a seconddevice.

Analyzing the received instruction-based equipment model at step 704further involves generating a geometric equipment model using thetime-series data based on the instruction-based equipment model. In someembodiments, the collected time series data is used to generate anN-dimensional graph where N is the total number of independent variables(e.g., one or more resources produced by a particular device) anddependent variables (e.g., one or more resources consumed by aparticular device). For example, a chiller device consumes electricalpower (one dependent variables) to produce chilled water (oneindependent variable) resulting in a two-dimensional geometric equipmentmodel.

Referring now to FIG. 8, a geometric equipment model 802 generated aspreviously described with reference to step 704 is shown, according toan exemplary embodiment. It should be understood that the type of deviceassociated with geometric equipment model 802 can include any type ofdevice operating in a central plant. For example, geometric equipmentmodel 802 may represent a heat pump model that consumes electrical power(represented by the y-axis) to produce a heat load (represented by thex-axis). As such, the geometric equipment model 802 includes twodimensions based on the total number of resources consumed and producedby the device (i.e., one dimension for each resource). However, aspreviously described, geometric equipment model 802 can include anynumber of dimensions based on the total number resources consumed andproduce by the device which a particular geometric equipment modelrepresents.

Geometric equipment model 802 is shown to include an operating domain804 comprising the actual operating points x₁, x₂, x₃ and any pointsthat can be interpolated between the actual operating points x₁, x₂, x₃(herein referred to as interpolated points). Each of the actualoperating points x₁, x₂, x₃ and corresponding interpolated points is anN-dimensional value that characterizes the operation of a device withwhich geometric equipment model 802 is associated. For example, thedevice characterized by geometric equipment model 802 contains twodimensions. As such, each of the actual operating points x₁, x₂, x₃ andinterpolated points are composed of two values (i.e., electric power andload Q_(x)). The number of sample points collected in a particular timeperiod is configurable based on user preference, equipment type,use-case, etc.

Although operating domain 804 shows to include three data points, anynumber of actual operating points may be included in operating domain804. Additionally, the size of operating domain 804 to group the samplepoints x₁, x₂, x₃ is configurable (e.g., configurable based on userpreference, time step between data samples, etc.). In some embodiments,geometric equipment model 802 includes more than one operating domain(e.g., operating domain 804) that groups different actual operatingpoints. For example, geometric equipment model 802 may include anadditional operating region (not shown) that is discrete from operatingdomain 804. An example of establishing operating domains is described inU.S. patent application Ser. No. 15/473,496 filed Mar. 29, 2017, theentire disclosure of which is incorporated by reference herein.Geometric equipment model 802 is also shown to include a non-operatingdomain 808 including a point x₀, according to some embodiments.Non-operating domain 808 may include a point located the origin of thegraph (e.g., [0,0]). Such point(s) may define a non-operating pointwhere the particular device or subplant is not operating (e.g., notconsuming a resource, not producing a resource).

Process 700 is shown to involve merging two or more geometric equipmentmodels to generate a geometric subplant model that comprises the two ormore geometric equipment models (step 706), according to someembodiments. In some embodiments, the two or more geometric equipmentmodels merged in step 706 are associated with devices that producesimilar resources (e.g., two or more chiller devices that producechilled water). In such embodiments, the dimensions of the mergedgeometric model are substantially equal to the total number of differentdimensions provided by the two or more geometric equipment models. Insome embodiments, the two or more geometric models merged in step 706are associated with at least one device that produces a differentresource (e.g., a chiller device that produces chilled water is mergedwith a hot water generator that produces hot water) and/or consumes adifferent resource. In such embodiments, the number of differentindependent variables and number of different dependent variables aresummed producing an N-dimensional geometric subplant model. In someembodiments, at least one of the dimensions included in theN-dimensional geometric subplant model is the electrical power consumedcombined by the one or more devices operating to produce respectiveindependent variables. N can be represented with the following equation:

N=D+I

where N is the total number of dimensions of the geometric subplantmodel, D is the number of different dependent variables of the two ormore devices being merged to form the geometric subplant model, and I isthe number of different independent variables of the two or more devicesbeing merged to form the geometric subplant model.

For example, a heater subplant contains a heat pump device operating toproduce an output resource of hot air (e.g., the independent variable ofthe heat pump device) and a boiler device operating to produce an outputresource of hot water (e.g., the independent variable of the boilerdevice). Each device consumes electrical power to produce its outputresource. As such, the geometric subplant model of the heater subplantincludes 3 dimensions: electrical power consumed by the heat pump deviceand the boiler device, hot air load produced by the heat pump device,and the hot water load produced by the boiler device.

Referring now to FIG. 9, an example of merging two geometric equipmentmodels to form a geometric subplant model is shown, according to anexemplary embodiment. FIG. 9 is shown to merge a first geometricequipment model 902 and a second geometric equipment model 904 to form ageometric subplant model 906 characterizing the operation of thesubplant that includes a first device associated with the firstgeometric equipment model 902 and a second device associated with thesecond geometric equipment model 904. Although FIG. 9 is shown toinclude two geometric equipment models, it should be understood that anynumber of geometric equipment models may be merged to form a geometricsubplant model.

First geometric equipment model 902 can be associated with any type ofdevice (e.g., chiller, boiler, cooling tower, etc.) included in thesubplant for which the geometric subplant model is being generated.Referring to the horizontal axis of first geometric equipment model 902,the first device x is shown to include an independent variable Q whichrepresents a type of resource produced according to the device type ofdevice x (e.g., chilled water for a chiller device, hot water for aboiler device). The vertical axis is shown to include a dependentvariable of electric power (e.g., the amount of electric power consumedby device x to produce a particular amount of resource Q). Likewise,second geometric equipment model 904 is shown to be associated with adevice y includes the same or substantially similar independent variableQ and dependent variable electrical power. For the purposes ofexplanation, the device x associated with first geometric equipmentmodel 902 and device y associated with second geometric equipment model904 are shown to be two discrete devices operating to produce the sameresource Q.

First geometric equipment model 902 is shown to include a firstoperating domain 908 comprising the operating points x₁, x₂, x₃ and anypoint that can be interpolated between the operating points x₁, x₂, x₃.First geometric equipment model 902 is also shown to include a firstnonoperating domain 910. The operating points and correspondinginterpolated points collected into the first operating domain 908 willbe included in the geometric subplant model 906 to provide solutions insituations where the device x is desired to produce the entirety of theamount defined by Q. Likewise, second geometric equipment model 904 isshown to include a second operating domain 912 comprising the operatingpoints y₁, y₂, y₃ and any point that can be interpolated between theoperating points Y₁, y₂, y₃. Second geometric equipment model 904 isalso shown to include a second nonoperating domain 914.

In the example illustrated in FIG. 9 and referring particularly to thegeometric subplant model 906, the operating points included in firstoperating domain 908 are summed with the operating points included inthe second operating domain 912 to form a total operating domain 916.The total operating domain 916 includes all combinations of summedoperating points included in first operating domain 908 and secondoperating domain 912 (e.g., x₁+y₁, x₁+y₂, x₂+y₁, etc.) and any pointsthat can be interpolated between each summed operating point.

In some embodiments, constraints are enforced which limit the number ofoperating points that are summed. The summation can be expressed withthe following equation:

x _(n) +y _(m) =t _(i)

where x_(n) is operating point n of device x (e.g., included in firstoperating domain 908) where n=1: N with N being the total number ofoperating points included in a particular first operating domain, y_(m)is operating point m (e.g., included in second operating domain 912) fordevice y where m=1: M with M being the total number of operating pointsincluded in a particular second operating domain, t_(i) is totaloperating point i (e.g., included in total operating domain 916), wherei=1:I with I being the total number of operating points included in aparticular operating domain. Total number of operating points I can befound with the following equation:

I=N*M

where I is the total number of operating points as previously described,N is the total number of operating points included in a first operatingdomain, and M is the total number of operating included in a secondoperating domain. In some embodiments in which constraints are enforcedthat limit the number of summed operating points, the product of N*M maybe less than 1.

Geometric subplant model 906 is shown to include four domains: firstoperating domain 908 (comprising solutions which require device x toproduce the entirety of an amount of resource Q), second operatingdomain 912 (comprising solutions which require device y to produce theentirety of an amount of resource Q), total operating domain 916(comprising solutions requiring both device x and device y operating toproduce an amount of resource Q), and non-operating domain 918 whichdefines a solution having no devices operating. As shown by the labeledoperating points included in total operating domain 916, the 9 totaloperating points include each summation combination of all operatingpoints included first operating domain 908 and all operating pointsincluded in second operating domain 912.

In some embodiments, the process of merging geometric equipment modelsassociated with two or more devices producing different resources toform a geometric subplant model as previously described can be extendedto produce a geometric central plant model that defines the operationalcharacteristics of a central plant. As such, in order to produce acentral plant model, two or more geometric subplant models can be mergedto form a geometric central plant model.

In some embodiments, two or more subplants are grouped together to formasubplant module defining the group of two or more subplants. In suchembodiments, the geometric subplant models associated with the two ormore subplants are merged together to form a geometric subplant modulemodel. For example, a hot water subplant may be merged with a chilledwater subplant to form a water subplant module. The technique of merginggeometric equipment models described with respect to FIGS. 7 and 9 maybe used to merge geometric subplant models to generate one or moregeometric subplant module model. As such, the one or more geometricsubplant module models may be merged to form a geometric central plant.Further, in some embodiments as will be described in greater detailbelow with reference to FIG. 10, a geometric central plant model may begenerated using a combination of geometric subplant module models andgeometric subplant models.

Referring now to FIG. 10, an example of merging two or more geometricsubplant models to form a geometric central plant model is shown,according to an exemplary embodiment. FIG. 10 is shown to merge a firstgeometric subplant model 1002 and a second geometric subplant model 1004to form a geometric central plant model 1006 characterizing theoperation of the central plant that includes a first subplant associatedwith the first geometric subplant model 1002 and a second subplantassociated with the second geometric subplant model 1004. Accordingly,in the embodiment illustrated in FIG. 10, the central plant associatedwith the geometric central plant model 1006 comprises only two subplants(e.g., a first subplant associated with the first geometric subplantmodel 1002 and a second subplant associated with the second geometricsubplant model 1004). However, it should be understood that the use oftwo subplants is for exemplary purposes only and not intended to belimiting. The process of generating a geometric central plant modelinvolves merging more than two geometric subplant models, according tosome embodiments. In some embodiments, generating a geometric centralplant model involves merging at least one geometric subplant modulemodel.

First geometric subplant model 1002 can be associated with any type ofsubplant (e.g., heater subplant, chiller subplant, electricity subplant,etc.) included in the central plant for which the geometric centralplant model is being generated. Likewise, second geometric subplantmodel 1004 can be associated with any type of subplant (e.g., heatersubplant, chiller subplant, electricity subplant, etc.) and may or maynot be substantially similar to the subplant associated with the firstsubplant associated with first geometric subplant model 1002. As shouldbe understood, the first geometric subplant model 1002 and secondgeometric subplant model 1004 may have been generated by the geometricmodeling module 518 performing step 702-step 708 of process 700.Although first geometric subplant model 1002 and second geometricsubplant model 1004 are shown to include two-dimensions, in someembodiments, first geometric subplant model 1002 and second geometricsubplant model 1004 include more than two dimensions.

Referring to the horizontal axis 1003 of first geometric subplant model1002, the first subplant is shown to include an independent variableQ_(e) which represents a type of resource produced according to thesubplant type associated with first geometric subplant model 1002 (e.g.,chilled water for a chiller subplant, hot water for a boiler subplant).The vertical axis 1005 of first geometric subplant model 1002 is shownto include a dependent variable of electric power (e.g., the amount ofelectric power consumed by the first subplant to produce a particularamount of resource Q). Second geometric subplant model 1004 isassociated with a second subplant operating to produce a differentresource (relative to the first subplant associated with the firstgeometric subplant model 1002) represented by the independent variableQ_(h).

First geometric subplant model 1002 is shown to include a firstoperating domain 1008 comprising the operating points x₁, x₂, x₃ and anypoints that can be interpolated between operating points x₁, x₂, x₃.First geometric subplant model 1002 is also shown to include a firstnonoperating domain 1010. The operating points collected into the firstoperating domain 1008 will be included in the geometric central plantmodel 1006 to provide solutions in situations in which the firstsubplant associated with the first geometric subplant model 1002 isdesired to produce an amount defined by Q_(e) while the second subplantassociated with the second geometric subplant model 1004 does notreceive an allocation. Likewise, the second geometric subplant model1004 is shown to include a second operating domain 1012 comprising theoperating points y₁, y₂, y₃ and any points that can be interpolatedbetween the operating points y₁, y₂, y₃. The second geometric subplantmodel 1004 is also shown to include a second nonoperating domain 1014.

In the example illustrated in FIG. 10, the geometric central plant model1006 is shown to include three dimensions (i.e., a first dimensionrepresented by a first axis Q_(e), a second dimension represented by asecond axis Q_(h), and a third dimension represented by a third axisP_(e)). As previously described, the number of dimensions included in ageometric central plant model is dependent upon the number of differentresources consumed and produced by the two or more geometric subplantmodels that merged together. For example, the subplant associated withfirst geometric subplant model 1002 consumes electrical power to producea resource represented by Q_(e) while the subplant associated withsecond geometric subplant model consumes electrical power to produce aresource represented by Q_(h). Accordingly, the three differentresources involved with the two subplants results in the geometriccentral plant model having three dimensions.

The total operating domain 1016 represents operating points at whichboth the first subplant associated with first geometric subplant model1002 and the second subplant associated with the second geometricsubplant model 1004 are operating to produce equal or non-equal amountsof their respective resources. The operating points included in thefirst operating domain 1008 are summed with the operating pointsincluded in the second operating domain 1012 to form the total operatingdomain 1016. The total operating domain 1016 includes all combinationsof summed operating points included in first operating domain 1008 andsecond operating domain 912 (e.g., x₁+y₁, x₁+y₂, x₂+y₁, etc.) and anypoints that can be interpolated between the summed operating points.Accordingly, the total operating domain 1016 is a surface. A graph 1018represents a two-dimensional reflection of the total operating domain1016 onto a plane defined by the horizontal axis 1003 and the verticalaxis 1005.

Referring back to FIG. 7, process 700 is shown to involve receiving aload allocation for a particular subplant at a particular time step(step 708), according to an exemplary embodiments. In some embodiments,step 710 involves receiving a load allocation for one or more subplantsas determined by asset allocator 402. In some embodiments, the loadallocation is received by destination specifier 608. The load allocationis received as a vector and can be represented with the followingequation:

$L_{N} = \begin{Bmatrix}N_{1} \\N_{2} \\\vdots \\N_{K}\end{Bmatrix}$

where A_(N) is the load allocation vector at a particular time step, N₁is the load allocation for subplant 1 at a particular time step, N₂ isthe load allocation for subplant 2 at a particular time step, and N_(K)is the load allocation for subplant K where the number of subplantsrange from k=1:K with K being the total number of subplants for which ageometric model was generated at a particular time step.

In some embodiments, each of the load allocation values N₁:N_(K) is fora particular subplant and is defined by a multidimensional coordinatevalue (e.g., x, y, z) where the number of dimensions included in themultidimensional coordinate value are substantially the same as thesample points included in the one or more geometric equipment modelsthat were merged to form the geometric subplant model for the particularsubplant. In some embodiments, the number of dimensions defining themultidimensional coordinate values of the load allocation are not equalto the dimensions of the sample points. For example, a two-dimensionalgeometric subplant model with a first dimension defining electric powerand a second dimensions defining chilled water load produced receives aone-dimensional load allocation defining only a value of chilled waterload produced (e.g., an electric power value is not included as part ofthe load allocation). In such embodiments, the geometric subplant modelis adjusted to remove one or more dimensions that are not defined by theload allocation. For example, with reference to FIG. 10 and relative tothe geometric central plant model 1006, the simplified graph 1018 doesnot include the third dimension (i.e., P_(E)). As a result, thegeometric modeling module 518 is able to receive a two-dimensional loadallocation and determine the nearest operating point without receiving avalue of P_(E). Upon finding the nearest operating point in thetwo-dimensional space, geometric modeling module 518 can look to thegeometric equipment models (e.g., first geometric subplant model 1002,second geometric subplant model 1004) to determine the electrical powerrequired by the associated devices to produce such a load defined by thenearest operating point.

Still referring to FIG. 7, process 700 is shown to involve specifying aparticular amount (e.g., a percentage) of the resource produced to bedivided (equally or non-equally) between two or more destinations thatare consumers of the produced resource (step 710), according to anexemplary embodiment. In some embodiments, the load allocation isreceived by destination specifier 608. In some embodiments in which anamount of a particular resource is divided between two or moreconsumers, destination specifier 608 determines each of the particularamounts of a resource to be divided between two or more consumers. Insuch embodiments, the total of the divided amounts of a resource aresubstantially equal to the amount of a resource defined by the receivedload allocation. In some embodiments in which a subplant only includesone device, step 710 may be skipped. Specifying a load allocationbetween two or more destinations can be expressed with the followingequation:

L _(t) *t _(d) =P _(d)

where L_(t) is the total load allocation allocated to a first subplant,t_(d) is the percentage of the load allocation to be transmitted toconsumer d, and P_(d) is the particular amount of the total loadallocation L_(t) to be transmitted to consumer d where the number ofdevices range d=1:D with D being the total number of devices in asubplant.

Referring now to FIG. 11, various graphs are shown illustrating theprocess of specifying two destinations for resources produced by asubplant (e.g., step 710 of process 700), according to an exemplaryembodiment. A geometric equipment model 1102 illustrates a geometricequipment model for a device (as can be generated with respect to step704). The geometric equipment model 1102 is shown to include anoperating domain 1103 comprising the sample points x₁, x₂, x₃. Each ofthe sample points define an operating point obtainable by the device orsubplant with which the geometric equipment model 802 is associated.

Referring to a second graph 1104, the operating domain 1103 is shown tobe applied each to a first destination axis 1108 and a seconddestination axis 1110, according to some embodiments. The firstdestination axis 1108 represents an amount of a resource to be portionedto a first destination L₁ (e.g., a first consumer of the resource).Accordingly, the second destination axis 1110 represents an amount of aresource to be portioned to a second destination L₂ (e.g., a secondconsumer of the resource). Each of the sample points x₁, x₂, x₃ on thefirst destination axis 1108 and the second destination axis 1110represents an entirety of the load defined by the sample points x₁, x₂,x₃ being allocated to the first destination L₁ and the seconddestination L₂, respectively.

The divided graph 1106 represents possible solutions for dividing eachsample point (e.g., x₁, x₂, etc.) between a first destination L₁ and asecond destination L₂. As previously described, any point along thefirst destination axis 1108 represents 100% of the load being allocatedto a first destination L₁. Accordingly, any point along the seconddestination axis 1110 represents 100% of the load being allocated to asecond destination L₂. The points 1112 and 1114 represent 100% of theload defined by x₃ being allocated to first destination L₁ and seconddestination L₂, respectively. Any point along the line 1116 has a firstdestination coordinate on the first destination axis 1108 and a seconddestination coordinate on the second destination axis 1110 that sum toequal to x₃. Accordingly, any point along the line 1118 has a firstdestination coordinate on the first destination axis 1108 and a seconddestination coordinate on the second destination axis 1110 that sum toequal to x₂. As such, any point along the line 1120 has a firstdestination coordinate on the first destination axis 1108 and a seconddestination coordinate on the second destination axis 1110 that sum toequal to x₁. This relationship can be represented by the followingequation:

Q _(T) =Q _(L1) +Q _(L2) +Q _(Ln)

where Q_(T) is the total load, Q_(L1) is the portion of Q_(T) allocatedto a first destination L₁, Q_(L2) is the portion of Q_(T) allocated to asecond destination L₂, and Q_(LN) is the portion of Q_(T) allocation todestination Ln, where the number of destinations ranges is n=1:N with Nbeing the total number of destinations

Referring back to FIG. 7, process 700 is shown to involve locating thenearest operating point on a geometric subplant model (step 712),according to an exemplary embodiment. In some embodiments, locating thenearest operating point involves nearest point analyzer 610 using theload allocation to a particular subplant to determine a nearestoperating point on the geometric subplant. In general, the process oflocating the nearest operating point on a geometric subplant modelinvolves determining an operating point on a geometric subplant modelwith the least amount of Euclidean distance between the value defined bythe load allocation and the operating point. The Euclidean distancebetween two points can be represented with the following equation:

${d\left( {p,q} \right)} = \sqrt{\sum\limits_{i = 1}^{n}\left( {q_{i} - p_{i}} \right)^{2}}$

where d(p, q) represents the distance between points p and q, prepresents a desired operating point defined by a load allocation, qrepresents an operating point located on the geometric subplant model,p_(i) is the ith coordinate of the desired operating point, q_(i) is theith coordinate of the actual operating point, and n represents the totalnumber of dimensions included in a model.

Referring now to FIG. 12, various graphs are shown illustrating theprocess of locating a nearest operating point on a geometric subplantmodel (e.g., step 712 of process 700) relative a desired operatingpoint, according to an exemplary embodiment. A geometric subplant model1202 generated as previously described is shown. The subplant for whichgeometric subplant model 1202 is shown to include three dimensions(i.e., P_(E), Q_(E), and Q_(H)). In the exemplary scenario illustratedin FIG. 12, the subplant modeled by geometric subplant model 1202receives a two-dimensional load allocation (e.g., a first dimensionbeing an allocation of Q_(E) and a second dimension being allocation ofQ_(H)). Advantageously, as will be described in greater detail below,based on the two dimensional load allocation received, nearest pointanalyzer 610 is able to locate a nearest operating point despite theload allocation comprising only two dimensions.

The solution graph 1204 represents a two-dimensional graph comprising aprojection of the operating points into the plane defined by the axisQ_(E) and Q_(H) (e.g., the third dimension defined by P_(E) is removedfrom the graph). Accordingly, solution graph 1204 is able to solve for anearest point using a load allocation comprising fewer dimensions thanthe geometric subplant model 1202. Solution graph 1204 is shown toinclude a desired operating point 1206 comprising a value (Q_(E), Q_(H))defined by a load allocation at a particular time step. The nearestpoint analyzer 610 calculated a Euclidean distance value (using thepreviously-described equation) between the desired operating point 1206and each actual operating point on or within the total operating domain1208. Nearest point analyzer 610 determined, based on the minimumEuclidean distance value calculated, that nearest operating point 1210is the nearest operating point with the minimum distance between thedesired operating point 1206 and the one or more actual operating pointpoints contain on or within the total operating domain 1208.

Referring back to FIG. 7, process 700 is shown to involve interpolatingthe nearest operating point to determine individual components of thenearest operating point (step 714). In some embodiments, the individualcomponents of the nearest operating point are determined by nearestpoint analyzer 610. In general, the individual components of the nearestoperating point define the amount of the load (as defined by the nearestoperating point) that each of the one or more devices included in thesubplant will produce. For example, performing step 714 determines thata first chiller device included in a chiller subplant will produce 70%of the load defined by the nearest operating point while a secondchiller device will produce 30% of the load.

Referring back to FIG. 12, the first point 1212 represents the amount ofthe load defined by the nearest operating point 1210 that first devicewill produce. The second point 1214 represents the amount of the loaddefined by the nearest operating point 1210 that second device willproduce. The first point 1212 and the second point 1214 can be used todetermine, on each point's respective equipment mode, the electric powerrequired by the device to produce such a load.

Referring back to FIG. 7, process 700 is shown to involve transmittingthe nearest operating point (or individual components of the nearestoperating point) for use in operating the one or more devices at thenearest operating point (step 716), according to an exemplaryembodiment. Step 716 may involve nearest point analyzer 610 outputtingthe nearest operating point to BMS 506 for use in controlling the one ormore devices at the nearest operating point.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

1. A method for operating a subplant included in a central plant, themethod comprising: obtaining one or more instruction-based equipmentmodels, wherein each of the one or more instruction-based equipmentmodels is associated with a particular device included in the subplantand comprises a plurality of operating points that define an operationof the particular device; generating, for each of the one or moreinstruction-based equipment models, a geometric equipment model usingthe plurality of operating points from a particular one of the one ormore instruction-based equipment models, the geometric equipment modeldefining at least one operating domain associated with the particulardevice; merging one or more geometric equipment models to form ageometric subplant model, the geometric subplant model defining anoperation of the subplant comprising one or more devices associated withthe one or more geometric equipment models; receiving a desiredoperating point comprising a load value; determining, relative to thedesired operating point, a nearest operating point on the geometricsubplant model; setting the nearest operating point on the geometricsubplant model as an actual operating point; and operating the subplantat the actual operating point for the subplant.
 2. The method of claim 1further comprising repeating the obtaining, generating, merging,receiving, determining, and setting steps for one or more subplantsincluded in the central plant to determine the actual operating pointfor each of the one or more subplants.
 3. The method of claim 1, whereingenerating the geometric equipment model comprises using a plurality oftime-series data points to generate the geometric equipment model. 4.The method of claim 3, wherein the plurality of time-series data pointsfurther comprises grouping data gathered from a real-time datacollection process.
 5. The method of claim 1, wherein merging one ormore of the geometric equipment models to form the geometric subplantmodel further comprises summing each operating point included in the oneor more geometric equipment models.
 6. The method of claim 1, furthercomprising defining two or more destinations of an amount of aparticular resource produced by the subplant by appointing a portion ofthe amount to each of the two or more destinations, wherein a summationof each portion is the amount of the particular resource.
 7. The methodof claim 1, wherein determining the nearest operating point on thegeometric subplant model further comprises determining a Euclideandistance value between the desired operating point and each of theplurality of operating points included in the geometric subplant model.8. The method of claim 7, wherein determining the nearest operatingpoint on the geometric subplant model further comprises determining anoperating point with a minimum Euclidean distance value.
 9. A controllerfor at least one subplant comprising one or more devices, the controllercomprising: a geometric modeling module configured to generate, for eachof the one or more devices included in each of the at least onesubplants, a geometric equipment model defining an operation of aparticular one of the one or more devices, wherein the geometricmodeling module comprises: an instruction-based model databaseconfigured to store one or more instruction-based equipment models,wherein each of the one or more instruction-based equipment models isassociated with a particular device included in one of the at least onesubplants and comprises a plurality of operating points that defines anoperation of the particular device; a geometric model generatorconfigured to generate, for each of the one or more instruction-basedequipment models, the geometric equipment model using the one or moreinstruction-based equipment models retrieved from the instruction-basedmodel database; a geometric model merger configured to merge at leasttwo geometric equipment models generated by the geometric modelgenerator and generate a geometric subplant model, wherein the geometricsubplant model is associated with one of the at least one subplants; anda nearest point analyzer configured to receive a desired operating pointand determine, on the geometric subplant model, a nearest operatingpoint relative to the desired operating point.
 10. The controller ofclaim 9, wherein the controller further comprises a destinationspecifier configured to receive the geometric subplant model anddetermine two or more destinations of an amount of a resource producedby the one or more devices by appointing a portion of the amount to eachof the two or more destinations.
 11. The controller of claim 9, whereinthe plurality of operating points comprises a plurality of sample pointsgathered by a real-time data collection process.
 12. The method of claim9, wherein the nearest point analyzer is configured to determine aEuclidean distance value between the desired operating point and each ofthe plurality of operating points included in the geometric subplantmodel.
 13. The method of claim 12, wherein the nearest point analyzer isfurther configured to determine the nearest operating point with aminimum Euclidean distance.
 14. A method for operating one or moresubplants included in a central plant, the method comprising: obtainingone or more instruction-based equipment models, wherein each of the oneor more instruction-based equipment models is associated with aparticular device included in one of the one or more subplants andcomprises a plurality of operating points that define an operation ofthe particular device; generating, for each of the one or moreinstruction-based equipment models, a geometric equipment model usingthe plurality of operating points from a particular one of the one ormore instruction-based equipment models, the geometric equipment modeldefining at least one operating domain associated with the particulardevice; merging two or more geometric equipment models to form ageometric subplant model, the geometric subplant model defining anoperation of a particular one of the one or more subplants; receiving adesired operating point comprising a load value; determining, relativeto the desired operating point, a nearest point on the geometricsubplant model; setting the nearest point on the geometric subplantmodel as an actual operating point; and operating at least one of theone or more subplants such that the at least one of the one or moresubplants produces an amount of a particular resource defined by theactual operating point.
 15. The method of claim 14, wherein generatingthe geometric equipment model comprises using a plurality of time-seriesdata points to generate the geometric equipment model.
 16. The method ofclaim 14, wherein merging the two or more geometric equipment models toform the geometric subplant model further comprises summing each of theplurality of operating points included in the two or more geometricequipment models.
 17. The method of claim 14, further comprisingdefining two or more destinations of a portion of the amount of aparticular resource produced by the one or more subplants by appointingthe portion of the amount to each of the two or more destinations,wherein a summation of each portion is the amount of the particularresource.
 18. The method of claim 14, wherein determining the nearestpoint on the geometric subplant model further comprises determining aEuclidean distance value between the desired operating point and each ofthe plurality of operating points included in the geometric subplantmodel.
 19. The method of claim 18, wherein determining the nearest pointon the geometric subplant model further comprises determining anoperating point with a minimum Euclidean distance value.
 20. The methodof claim 14, further comprising repeating the obtaining, generating,merging, receiving, determining, and setting steps for each of the oneor more subplants included in the central plant to determine the actualoperating point for each of the one or more subplants.